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2 дня назад

Data Scientist, Product Analytics (AI)

Формат работы
onsite
Тип работы
fulltime
Английский
b2
Страна
UK/Singapore/US +4 еще
Релокация
Switzerland
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Data Scientist, Product Analytics (AI): Analyze product usage and connect it with customer and sales data to generate insights for product adoption, retention, expansion, and GTM decisions with an accent on statistical analysis, experimentation, and usage-based health signals. Focus on modeling product and commercial data, validating predictive signals, and communicating recommendations to product and business stakeholders.

Location: Geneva, Switzerland. Workplace: On-site, with anchor days on Mondays, Tuesdays, and Thursdays. Candidates must be based in Geneva or be willing to relocate.

Company

hirify.global develops AI code verification and governance products, including hirify.globalQube, for reliable, secure, and maintainable software development.

What you will do

  • Analyze product adoption, feature usage, engagement patterns, and retention in partnership with product managers.
  • Connect product usage data with customer, CRM, and sales data to assess expansion, churn risk, and account health.
  • Build usage-based scoring logic, health indicators, and metrics supporting activation, retention, and expansion.
  • Explore data proactively, identify actionable findings, and run experiments using statistical methods.
  • Partner with Data and Analytics Engineers on warehouse requirements, data models, and product-to-commercial data foundations.
  • Communicate insights, recommendations, caveats, and decisions clearly to product and business stakeholders.

Requirements

  • Strong product understanding and experience partnering with product managers to influence decisions.
  • Demonstrated analytical impact on revenue, product, or go-to-market decisions.
  • Solid SQL skills for independent querying, joining, and data exploration.
  • Proficiency in Python for analysis, modeling, and automation, with experience using machine learning and statistical libraries such as scikit-learn and statsmodels.
  • Knowledge of experimentation, significance testing, regression, segmentation, forecasting, funnels, cohorts, retention, and account health.
  • Strong communication, stakeholder management, autonomy, and willingness to work hands-on with data modeling and AI tools.

Culture & Benefits

  • In-office collaboration is built around three anchor days each week: Monday, Tuesday, and Thursday.
  • Relocation support is available for the right candidate.
  • Inclusive workplace committed to diversity, equity, and inclusion.
  • Employment is subject to a background check and reference verification.

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